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  本文關鍵詞：灰色-GARCH混合模型及其在股票指數中的應用　出處：《西北農林科技大學》2012年碩士論文　論文類型：學位論文

  更多相關文章： 灰色-GARCH混合模型 GM模型 GARCH模型

【摘要】：改革開放以來，中國的金融證券市場得到了很好的和完善的發(fā)展，無論男女老少越來越多的國民參與進來，形成了人人參與股票投資的熱潮，隨著股市的跌宕起伏或喜或悲，對于普通的股民，股票市場的風云變幻一直是他們心中既怕又愛的雙刃劍，對于投資機構和大股東而言，股票市場的波動是他們規(guī)避風險的重要依據，對于市場監(jiān)管機構來說，股市的波動一直是其市場監(jiān)管有效性的重要度量和市場政策制定的重要依據。由此可見，波動率的建模和描述一直都是各方關注的焦點和重點，對學者和股市從業(yè)者都有極其重要的意義,并且波動率的計算也為VaR數學模型的建立和計算提供了依據和基礎。 因此，為了描述和刻畫市場的波動，本文在時間序列模型其中主要是廣義自回歸條件異方差模型和灰色模型基礎上，認真研究和總結了以前學者和專家的研究成果，提出了新陳代謝灰色-廣義自回歸條件異方差混合模型，即灰色-GARCH混合模型。以前的研究結果表明，廣義自回歸條件異方差模型的殘差項應會隨著時間的變動而受到過去價格波動或信息沖擊等灰色不確定性因素的影響，并隨之變化，這對于廣義自回歸條件異方差模型來說是一個很難明確描述和表達的變量，其結果就是直接的影響了廣義自回歸條件異方差模型對于波動率的刻畫和估計。因此本文采用灰色系統理論的以少量數據資料即能建立起不錯的預測模型和對灰色不確定性因素的良好描述和預測等良好特性，對廣義自回歸條件異方差模型內的殘差項建立灰色模型，用這兩個模型得到灰色-GARCH混合模型，用它來重新描述和估計市場的波動。因為此模型應用到灰色模型和廣義自回歸條件異方差模型，是這兩個模型的有機的結合，具有灰色模型對灰色信息的良好撲捉和廣義自回歸條件異方差模型對波動率的很好的表達，所以叫做灰色-GARCH混合模型。 為了建立灰色-GARCH混合模型，本文首先介紹了時間序列模型和灰色模型的發(fā)展和其現在的研究現狀，并且對這兩類模型的建模步驟和方法進行了比較全面的介紹，在其基礎上本文建立了灰色-GARCH混合模型，隨后采用道瓊斯中國網站數據，運用Eviews軟件和Matlab軟件，，對選取的道瓊斯中國88指數的數據進行了實證分析，結果表明，與廣義自回歸條件異方差模型相比較，本文所建立的灰色-GARCH混合模型對波動的表達更貼近市場實際，其對波動率的描述和刻畫也更加準確，并且對于在此基礎上建立的VaR數學模型的準確性提供了可靠的依據和數據保證。
[Abstract]:Since the reform and opening up, China's financial and securities market has been a very good and perfect development, regardless of the men, women and children more and more citizens participate in the formation of everyone's participation in stock investment upsurge. With the ups and downs of the stock market or happy or sad, for the ordinary shareholders, the stock market has been changing in their hearts both afraid and love double-edged sword, for investment institutions and major shareholders. The volatility of the stock market is an important basis for them to avoid risks. For the market regulators, the volatility of the stock market has always been an important measure of the effectiveness of their market regulation and an important basis for the formulation of market policies. The modeling and description of volatility has always been the focus and focus of all parties concerned, which is of great significance to scholars and stock market practitioners. The calculation of volatility also provides the basis for the establishment and calculation of VaR mathematical model. Therefore, in order to describe and characterize the volatility of the market, this paper based on the time series model, mainly generalized autoregressive conditional heteroscedasticity model and grey model. This paper studies and summarizes the research results of previous scholars and experts, and puts forward the mixed model of metabolism gray and generalized autoregressive conditional heteroscedasticity, that is, the grey GARCH mixed model. The residual term of generalized autoregressive conditional heteroscedasticity model should be affected by grey uncertainty such as price fluctuation or information shock with time change. For the generalized autoregressive conditional heteroscedasticity model, it is difficult to describe and express the variables clearly. The result is that it directly affects the characterization and estimation of volatility in generalized autoregressive conditional heteroscedasticity model. Therefore, the grey system theory can be used to establish a good prediction model and grey model with a small amount of data. Good description and prediction of color uncertainty. The grey model is established for the residual terms in the generalized autoregressive conditional heteroscedasticity model and the grey GARCH mixed model is obtained by using these two models. It is used to redescribe and estimate the volatility of the market because the application of this model to the grey model and the generalized autoregressive conditional heteroscedasticity model is an organic combination of the two models. The grey GARCH mixed model is called the grey GARCH mixed model because of the good capture of grey information by grey model and the good representation of volatility by generalized autoregressive conditional heteroscedasticity model. In order to establish the grey GARCH mixed model, this paper firstly introduces the development of the time series model and the grey model and its present research status. And the modeling steps and methods of these two models are introduced comprehensively. On the basis of these models, the grey GARCH mixed model is established, and then the Dow Jones website data is used. Using Eviews software and Matlab software, the data of Dow Jones China 88 index are analyzed. The results show that the data are compared with the generalized autoregressive conditional heteroscedasticity model. The grey GARCH hybrid model presented in this paper is more close to the market reality and its description and characterization of volatility is more accurate. It also provides a reliable basis and data guarantee for the accuracy of the VaR mathematical model established on this basis.
【學位授予單位】：西北農林科技大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：F224;F832.51

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